In the prior art the distributed control solution to a complex process incorporating various hardware components, for example sensors and actuators, involved hand generating reams of software code specially adapted to the computing platform and the exact specification of the physical process. Even relatively simple, and similar, distributed control solutions were invariably built from the ground up, and every alteration of hardware of the process therefore required laborious, time-consuming evaluation of the impact of each such change on the control solution. Typically, weeks or even months of effort have been spent configuring a plant-wide process control system, and days or even weeks needed to make changes to the control system following a change of device components, often requiring a stoppage of the process and a reevaluation of the impact of the change on remote potions of the controlled process. Also, prior art approaches inevitably lose all their structure and interrelationships once their software code is compiled, thus requiring lengthly efforts to recreate the prior defined data structure and interrelationships.
Traditionally, control engineers delved into the details of computing that have little to do with process control theory or practical solutions to make the algorithms execute correctly. This has largely been because general purpose operating systems do not provide a useful set of high level design abstractions for the control domain. For example, the ability to easily detect specific control related events or schedule tasks appropriately for more advanced, multivariable control laws. This has resulted in monolithic control programs that have calls to the operating system buried within control loops.